AI is transforming supply chain operations from demand forecasting to fleet optimization. New ABI Research survey results reveal that most supply chain leaders view AI, Gen AI, and Agentic AI as essential for smarter, faster, and more autonomous decision-making.
Key Takeaways:
- AI adoption is now a priority in supply chains. Sixty-four percent of supply chain leaders say Artificial Intelligence (AI) and Generative AI (Gen AI) capabilities are important when evaluating new technology investments.
- Decision support leads AI use cases. Ninety-four percent of companies plan to use AI or Gen AI to assist with decision-making, showing the growing trust in AI-driven recommendations.
- Customer service and demand forecasting follow closely. Over 90% of respondents plan to use AI tools to enhance customer support and improve forecasting accuracy.
- Agentic AI is emerging for supplier management. Seventy-six percent of professionals see potential for autonomous AI agents to handle tasks like reordering and shipment rerouting.
- Machine Vision (MV) is gaining momentum. More than half of manufacturers plan to invest over US$100,000 in AI-powered camera systems for warehouse efficiency, quality, and safety.
There are countless blogs on the Internet explaining how Artificial Intelligence (AI) is being used in supply chain management. But the most accurate assessment will come straight from the horse’s mouth.
ABI Research recently surveyed 490 supply chain management professionals across the United States, Mexico, Germany, and Malaysia. We asked them a series of questions about their perception of AI and their investment plans.
One of the most striking stats was that 64% of supply chain leaders say that having AI/Gen AI capabilities is important or very important when evaluating a new technology solution. Let’s take a closer look at where they find the most value in AI, Gen AI, and Agentic AI.
How do supply chains plan to use AI?
Using our survey results, we identify the top ways in which supply chain managers plan to leverage AI/Gen AI.
- Decision Support: The largest demand for AI applications in the supply chain lies in decision support, particularly in the form of system-generated recommendations. Ninety-four percent of respondents told us they plan to use AI or Gen AI for decision support over the next two years.
- Customer Service: The second most popular way AI is used is in customer service. It’s been a low-hanging fruit use case for Gen AI, especially for chatbots that resolve customer inquiries. Ninety-one percent of respondents told us they plan to use AI or Gen AI for customer service over the next 2 years.
- Demand Forecasting: Ninety-one percent of respondents stated they plan to use AI or Gen AI for demand forecasting over the next 2 years. Customer service just edges out demand forecasting in terms of Gen AI utilization.
- Inventory Management: Eighty-five percent of supply chain leaders show an inclination to use AI for inventory management.
Chart 1: Please indicate your plans to use or not use AI and Gen AI in the following areas over the next 2 years
Where is Gen AI not being used as much?
While traditional AI is still very popular across all use cases, Gen AI is less likely to be deployed for others. Just 27% of respondents plan to use Gen AI for product design & planning, 30% for transport optimization and compliance monitoring, 31% for supply chain network design, and 37% for risk management.
These applications have high investment expectations for traditional AI, which is usually sufficient alongside Machine Learning (ML).
What will supply chains use Agentic AI for?
Supplier relationship management ranked as the top use case for Agentic AI in supply chain management, with 76% of respondents agreeing that AI agents can be used for it. This includes automatic reordering and shipment re-routing capabilities.
While responses were relatively consistent across industry and company size, we did notice a regional deviation. Among U.S.-based respondents, 60.5% agree or strongly agree that text-to-code capabilities are a key use case for Agentic AI. For their counterparts in Germany, Mexico, and Malaysia, that number is less than half.
My recent report, Artificial Intelligence (AI) in the Supply Chain: Fleet Management, offers more insight into the use of AI in the supply chain . “Trained AI agents can autonomously manage specific fleet tasks, monitoring fleet performance, scheduling repairs, and optimizing downtimes.” The report continues, “More advanced Agentic AI systems proactively monitor, analyze, and optimize vehicle operations without constant human intervention.”
Chart 2: Please indicate your plans to use or not use Agentic AI in the following areas over the next 2 years.
How much money are supply chains investing in machine vision?
Rapid advancements in AI and Machine Learning (ML) have enabled cameras to become valuable tools for warehouse operators. MV-enabled cameras are used for efficiency, quality, safety, digital twins, and asset tracking.
Manufacturing firms plan to invest the most in MV solutions, with 55% of manufacturers saying they will invest more than US$100,000 over the next 2 years. For retailers and Third-Party Logistics (3PL) providers, the figures are 49% and 37%, respectively. Interestingly, retailers are the most likely to invest more than US$250,000 in MV.
Chart 3: What level of machine vision investment do you currently have planned in the following warehousing and fulfillment technologies over the next 2 years?
Download the full survey
AI and the more advanced Gen AI/Agentic AI are transforming the supply chain like nearly every other industry. These tools are shaking up the way customers view supply chain software solutions, requiring vendors to accelerate innovation.
For a more comprehensive assessment of how supply chain professionals plan to leverage AI and other technologies, refer to ABI Research's 2025 Technology In Supply Chain Management & Logistics Survey Results.